Overview

Dataset statistics

Number of variables31
Number of observations89551
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.2 MiB
Average record size in memory248.0 B

Variable types

Numeric16
Categorical15

Alerts

CEP_2_DIG is highly overall correlated with ZONA_POSTALHigh correlation
RENDA_MES_ANTERIOR is highly overall correlated with FLAG_PFHigh correlation
NO_FUNCIONARIOS is highly overall correlated with FLAG_PFHigh correlation
PRAZO is highly overall correlated with PAGTO_DIFF_EMISSAOHigh correlation
PAGTO_DIFF_EMISSAO is highly overall correlated with PRAZOHigh correlation
LIFETIME_CLIENTE_DIAS is highly overall correlated with ANO_CADASTROHigh correlation
MES_EMISSAO_DOCUMENTO is highly overall correlated with MES_PAGAMENTO and 2 other fieldsHigh correlation
MES_PAGAMENTO is highly overall correlated with MES_EMISSAO_DOCUMENTO and 2 other fieldsHigh correlation
MES_VENCIMENTO is highly overall correlated with MES_EMISSAO_DOCUMENTO and 2 other fieldsHigh correlation
MES_SAFRA_REF is highly overall correlated with MES_EMISSAO_DOCUMENTO and 2 other fieldsHigh correlation
ANO_VENCIMENTO is highly overall correlated with ANO_EMISSAO_DOCUMENTO and 2 other fieldsHigh correlation
ANO_CADASTRO is highly overall correlated with LIFETIME_CLIENTE_DIASHigh correlation
FLAG_PF is highly overall correlated with RENDA_MES_ANTERIOR and 1 other fieldsHigh correlation
ZONA_POSTAL is highly overall correlated with CEP_2_DIGHigh correlation
DIASEMANA_PAGAMENTO is highly overall correlated with DIASEMANA_VENCIMENTOHigh correlation
DIASEMANA_VENCIMENTO is highly overall correlated with DIASEMANA_PAGAMENTOHigh correlation
ANO_EMISSAO_DOCUMENTO is highly overall correlated with ANO_VENCIMENTO and 2 other fieldsHigh correlation
ANO_PAGAMENTO is highly overall correlated with ANO_VENCIMENTO and 2 other fieldsHigh correlation
ANO_SAFRA_REF is highly overall correlated with ANO_VENCIMENTO and 2 other fieldsHigh correlation
FLAG_PF is highly imbalanced (97.1%)Imbalance
INADIMPLENTE is highly imbalanced (64.8%)Imbalance
PRAZO is highly skewed (γ1 = 48.05204434)Skewed
PAGTO_DIFF_VENC is highly skewed (γ1 = -52.78006706)Skewed
PAGTO_DIFF_VENC has 70609 (78.8%) zerosZeros

Reproduction

Analysis started2023-04-25 11:23:26.892953
Analysis finished2023-04-25 11:25:07.216574
Duration1 minute and 40.32 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

ID_CLIENTE
Real number (ℝ)

Distinct1306
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6635418 × 1018
Minimum8.7842371 × 1015
Maximum9.2060308 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:07.378939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.7842371 × 1015
5-th percentile4.6530925 × 1017
Q12.3470288 × 1018
median4.817817 × 1018
Q36.9693486 × 1018
95-th percentile8.6099387 × 1018
Maximum9.2060308 × 1018
Range9.1972466 × 1018
Interquartile range (IQR)4.6223198 × 1018

Descriptive statistics

Standard deviation2.6662324 × 1018
Coefficient of variation (CV)0.57171834
Kurtosis-1.2303272
Mean4.6635418 × 1018
Median Absolute Deviation (MAD)2.3501716 × 1018
Skewness-0.078049453
Sum8.9911379 × 1018
Variance7.108795 × 1036
MonotonicityNot monotonic
2023-04-25T11:25:07.679405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.96410875 × 10181325
 
1.5%
5.761480994 × 10181222
 
1.4%
4.008627435 × 1018902
 
1.0%
8.173830875 × 1018787
 
0.9%
6.916556752 × 1018724
 
0.8%
7.930925884 × 1018660
 
0.7%
4.592906068 × 1017620
 
0.7%
4.045739495 × 1018612
 
0.7%
7.325545719 × 1018581
 
0.6%
3.355881108 × 1018575
 
0.6%
Other values (1296) 81543
91.1%
ValueCountFrequency (%)
8.78423715 × 1015266
0.3%
1.507004831 × 101611
 
< 0.1%
1.871961495 × 10169
 
< 0.1%
3.954702544 × 101680
 
0.1%
4.326664122 × 10169
 
< 0.1%
4.963290558 × 101650
 
0.1%
6.62200874 × 101671
 
0.1%
6.97663625 × 101652
 
0.1%
8.611006299 × 1016103
 
0.1%
8.643695504 × 10161
 
< 0.1%
ValueCountFrequency (%)
9.20603081 × 1018123
0.1%
9.205015187 × 101828
 
< 0.1%
9.184785003 × 1018150
0.2%
9.175443729 × 10187
 
< 0.1%
9.161263096 × 101836
 
< 0.1%
9.156666134 × 101850
 
0.1%
9.142266044 × 101810
 
< 0.1%
9.127318191 × 101868
0.1%
9.108733472 × 101871
0.1%
9.101617111 × 101821
 
< 0.1%

VALOR_A_PAGAR
Real number (ℝ)

Distinct78917
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49201.267
Minimum0.1
Maximum4400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:08.007568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3643.54
Q119663.235
median36300.7
Q364065.3
95-th percentile135757.74
Maximum4400000
Range4399999.9
Interquartile range (IQR)44402.065

Descriptive statistics

Standard deviation48051.163
Coefficient of variation (CV)0.9766245
Kurtosis860.63904
Mean49201.267
Median Absolute Deviation (MAD)19077.65
Skewness13.012477
Sum4.4060227 × 109
Variance2.3089143 × 109
MonotonicityNot monotonic
2023-04-25T11:25:08.490203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182 624
 
0.7%
999 338
 
0.4%
945.6 198
 
0.2%
360 137
 
0.2%
1341 94
 
0.1%
591 76
 
0.1%
499 72
 
0.1%
1063.8 57
 
0.1%
1064 48
 
0.1%
899 43
 
< 0.1%
Other values (78907) 87864
98.1%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.4 1
< 0.1%
0.45 1
< 0.1%
0.7 1
< 0.1%
5.17 1
< 0.1%
5.5 1
< 0.1%
5.78 1
< 0.1%
5.95 1
< 0.1%
6 1
< 0.1%
6.22 1
< 0.1%
ValueCountFrequency (%)
4400000 1
< 0.1%
2250000 1
< 0.1%
1697544.07 1
< 0.1%
1500000 1
< 0.1%
1391835.2 1
< 0.1%
1325000 1
< 0.1%
1210000 1
< 0.1%
1200000 1
< 0.1%
1160000 1
< 0.1%
1000000 1
< 0.1%

TAXA
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
5.99
30349 
6.99
25410 
4.99
18355 
8.99
9281 
11.99
6156 

Length

Max length5
Median length4
Mean length4.068743
Min length4

Characters and Unicode

Total characters364360
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.99
2nd row6.99
3rd row6.99
4th row6.99
5th row6.99

Common Values

ValueCountFrequency (%)
5.99 30349
33.9%
6.99 25410
28.4%
4.99 18355
20.5%
8.99 9281
 
10.4%
11.99 6156
 
6.9%

Length

2023-04-25T11:25:08.997339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:09.540477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.99 30349
33.9%
6.99 25410
28.4%
4.99 18355
20.5%
8.99 9281
 
10.4%
11.99 6156
 
6.9%

Most occurring characters

ValueCountFrequency (%)
9 179102
49.2%
. 89551
24.6%
5 30349
 
8.3%
6 25410
 
7.0%
4 18355
 
5.0%
1 12312
 
3.4%
8 9281
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274809
75.4%
Other Punctuation 89551
 
24.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 179102
65.2%
5 30349
 
11.0%
6 25410
 
9.2%
4 18355
 
6.7%
1 12312
 
4.5%
8 9281
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 89551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 364360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 179102
49.2%
. 89551
24.6%
5 30349
 
8.3%
6 25410
 
7.0%
4 18355
 
5.0%
1 12312
 
3.4%
8 9281
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 179102
49.2%
. 89551
24.6%
5 30349
 
8.3%
6 25410
 
7.0%
4 18355
 
5.0%
1 12312
 
3.4%
8 9281
 
2.5%

FLAG_PF
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
PJ
89289 
PF
 
262

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters179102
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPJ
2nd rowPJ
3rd rowPJ
4th rowPJ
5th rowPJ

Common Values

ValueCountFrequency (%)
PJ 89289
99.7%
PF 262
 
0.3%

Length

2023-04-25T11:25:09.944320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:10.349723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pj 89289
99.7%
pf 262
 
0.3%

Most occurring characters

ValueCountFrequency (%)
P 89551
50.0%
J 89289
49.9%
F 262
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 179102
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 89551
50.0%
J 89289
49.9%
F 262
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 179102
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 89551
50.0%
J 89289
49.9%
F 262
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 89551
50.0%
J 89289
49.9%
F 262
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Serviços
36267 
Comércio
31630 
Indústria
20026 
SEM_VALOR
 
1628

Length

Max length9
Median length8
Mean length8.2418063
Min length8

Characters and Unicode

Total characters738062
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowServiços
2nd rowServiços
3rd rowServiços
4th rowServiços
5th rowServiços

Common Values

ValueCountFrequency (%)
Serviços 36267
40.5%
Comércio 31630
35.3%
Indústria 20026
22.4%
SEM_VALOR 1628
 
1.8%

Length

2023-04-25T11:25:10.679354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:11.210608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
serviços 36267
40.5%
comércio 31630
35.3%
indústria 20026
22.4%
sem_valor 1628
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o 99527
13.5%
r 87923
11.9%
i 87923
11.9%
s 56293
 
7.6%
S 37895
 
5.1%
v 36267
 
4.9%
ç 36267
 
4.9%
e 36267
 
4.9%
C 31630
 
4.3%
m 31630
 
4.3%
Other values (16) 196440
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 635487
86.1%
Uppercase Letter 100947
 
13.7%
Connector Punctuation 1628
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 99527
15.7%
r 87923
13.8%
i 87923
13.8%
s 56293
8.9%
v 36267
 
5.7%
ç 36267
 
5.7%
e 36267
 
5.7%
m 31630
 
5.0%
é 31630
 
5.0%
c 31630
 
5.0%
Other values (5) 100130
15.8%
Uppercase Letter
ValueCountFrequency (%)
S 37895
37.5%
C 31630
31.3%
I 20026
19.8%
E 1628
 
1.6%
M 1628
 
1.6%
V 1628
 
1.6%
A 1628
 
1.6%
L 1628
 
1.6%
O 1628
 
1.6%
R 1628
 
1.6%
Connector Punctuation
ValueCountFrequency (%)
_ 1628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 736434
99.8%
Common 1628
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 99527
13.5%
r 87923
11.9%
i 87923
11.9%
s 56293
 
7.6%
S 37895
 
5.1%
v 36267
 
4.9%
ç 36267
 
4.9%
e 36267
 
4.9%
C 31630
 
4.3%
m 31630
 
4.3%
Other values (15) 194812
26.5%
Common
ValueCountFrequency (%)
_ 1628
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650139
88.1%
None 87923
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 99527
15.3%
r 87923
13.5%
i 87923
13.5%
s 56293
8.7%
S 37895
 
5.8%
v 36267
 
5.6%
e 36267
 
5.6%
C 31630
 
4.9%
m 31630
 
4.9%
c 31630
 
4.9%
Other values (13) 113154
17.4%
None
ValueCountFrequency (%)
ç 36267
41.2%
é 31630
36.0%
ú 20026
22.8%

DOMINIO_EMAIL
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
YAHOO
29357 
GMAIL
25192 
HOTMAIL
21210 
OUTLOOK
5491 
AOL
5103 
Other values (2)
3198 

Length

Max length9
Median length5
Mean length5.482362
Min length3

Characters and Unicode

Total characters490951
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYAHOO
2nd rowYAHOO
3rd rowYAHOO
4th rowYAHOO
5th rowYAHOO

Common Values

ValueCountFrequency (%)
YAHOO 29357
32.8%
GMAIL 25192
28.1%
HOTMAIL 21210
23.7%
OUTLOOK 5491
 
6.1%
AOL 5103
 
5.7%
BOL 2132
 
2.4%
SEM_VALOR 1066
 
1.2%

Length

2023-04-25T11:25:11.641942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:12.023498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yahoo 29357
32.8%
gmail 25192
28.1%
hotmail 21210
23.7%
outlook 5491
 
6.1%
aol 5103
 
5.7%
bol 2132
 
2.4%
sem_valor 1066
 
1.2%

Most occurring characters

ValueCountFrequency (%)
O 104698
21.3%
A 81928
16.7%
L 60194
12.3%
H 50567
10.3%
M 47468
9.7%
I 46402
9.5%
Y 29357
 
6.0%
T 26701
 
5.4%
G 25192
 
5.1%
U 5491
 
1.1%
Other values (7) 12953
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 489885
99.8%
Connector Punctuation 1066
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 104698
21.4%
A 81928
16.7%
L 60194
12.3%
H 50567
10.3%
M 47468
9.7%
I 46402
9.5%
Y 29357
 
6.0%
T 26701
 
5.5%
G 25192
 
5.1%
U 5491
 
1.1%
Other values (6) 11887
 
2.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 489885
99.8%
Common 1066
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 104698
21.4%
A 81928
16.7%
L 60194
12.3%
H 50567
10.3%
M 47468
9.7%
I 46402
9.5%
Y 29357
 
6.0%
T 26701
 
5.5%
G 25192
 
5.1%
U 5491
 
1.1%
Other values (6) 11887
 
2.4%
Common
ValueCountFrequency (%)
_ 1066
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 490951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 104698
21.3%
A 81928
16.7%
L 60194
12.3%
H 50567
10.3%
M 47468
9.7%
I 46402
9.5%
Y 29357
 
6.0%
T 26701
 
5.4%
G 25192
 
5.1%
U 5491
 
1.1%
Other values (7) 12953
 
2.6%

PORTE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
MEDIO
34660 
GRANDE
33575 
PEQUENO
18468 
SEM_VALOR
 
2848

Length

Max length9
Median length7
Mean length5.9145962
Min length5

Characters and Unicode

Total characters529658
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPEQUENO
2nd rowPEQUENO
3rd rowPEQUENO
4th rowPEQUENO
5th rowPEQUENO

Common Values

ValueCountFrequency (%)
MEDIO 34660
38.7%
GRANDE 33575
37.5%
PEQUENO 18468
20.6%
SEM_VALOR 2848
 
3.2%

Length

2023-04-25T11:25:12.465426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:12.999091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
medio 34660
38.7%
grande 33575
37.5%
pequeno 18468
20.6%
sem_valor 2848
 
3.2%

Most occurring characters

ValueCountFrequency (%)
E 108019
20.4%
D 68235
12.9%
O 55976
10.6%
N 52043
9.8%
M 37508
 
7.1%
R 36423
 
6.9%
A 36423
 
6.9%
I 34660
 
6.5%
G 33575
 
6.3%
P 18468
 
3.5%
Other values (6) 48328
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 526810
99.5%
Connector Punctuation 2848
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 108019
20.5%
D 68235
13.0%
O 55976
10.6%
N 52043
9.9%
M 37508
 
7.1%
R 36423
 
6.9%
A 36423
 
6.9%
I 34660
 
6.6%
G 33575
 
6.4%
P 18468
 
3.5%
Other values (5) 45480
8.6%
Connector Punctuation
ValueCountFrequency (%)
_ 2848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 526810
99.5%
Common 2848
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 108019
20.5%
D 68235
13.0%
O 55976
10.6%
N 52043
9.9%
M 37508
 
7.1%
R 36423
 
6.9%
A 36423
 
6.9%
I 34660
 
6.6%
G 33575
 
6.4%
P 18468
 
3.5%
Other values (5) 45480
8.6%
Common
ValueCountFrequency (%)
_ 2848
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 529658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 108019
20.4%
D 68235
12.9%
O 55976
10.6%
N 52043
9.8%
M 37508
 
7.1%
R 36423
 
6.9%
A 36423
 
6.9%
I 34660
 
6.5%
G 33575
 
6.3%
P 18468
 
3.5%
Other values (6) 48328
9.1%

CEP_2_DIG
Real number (ℝ)

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.329421
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:13.546492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile13
Q129
median54
Q379
95-th percentile95
Maximum99
Range88
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.86701
Coefficient of variation (CV)0.52254476
Kurtosis-1.414858
Mean53.329421
Median Absolute Deviation (MAD)25
Skewness0.017584824
Sum4775703
Variance776.57023
MonotonicityNot monotonic
2023-04-25T11:25:14.044766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 4525
 
5.1%
35 3256
 
3.6%
68 3045
 
3.4%
89 3029
 
3.4%
37 2457
 
2.7%
86 2408
 
2.7%
78 2265
 
2.5%
12 2256
 
2.5%
75 2073
 
2.3%
38 1952
 
2.2%
Other values (79) 62285
69.6%
ValueCountFrequency (%)
11 1039
 
1.2%
12 2256
2.5%
13 4525
5.1%
14 1596
 
1.8%
15 1429
 
1.6%
16 811
 
0.9%
17 905
 
1.0%
18 757
 
0.8%
19 1010
 
1.1%
20 583
 
0.7%
ValueCountFrequency (%)
99 982
1.1%
98 1244
1.4%
97 539
 
0.6%
96 828
0.9%
95 1366
1.5%
94 256
 
0.3%
93 1052
1.2%
92 349
 
0.4%
91 193
 
0.2%
90 590
0.7%

RENDA_MES_ANTERIOR
Real number (ℝ)

Distinct1306
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291079.8
Minimum1861
Maximum918421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:14.490842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile228440.26
Q1267180.55
median291396.13
Q3314952.45
95-th percentile356990.9
Maximum918421
Range916560
Interquartile range (IQR)47771.897

Descriptive statistics

Standard deviation43720.533
Coefficient of variation (CV)0.15020119
Kurtosis7.9999776
Mean291079.8
Median Absolute Deviation (MAD)24109.67
Skewness-0.30489839
Sum2.6066487 × 1010
Variance1.911485 × 109
MonotonicityNot monotonic
2023-04-25T11:25:14.786260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288175.9487 1325
 
1.5%
282459.525 1222
 
1.4%
307876.6923 902
 
1.0%
291017.1667 787
 
0.9%
258061.275 724
 
0.8%
306436.025 660
 
0.7%
309266.025 620
 
0.7%
245928.4 612
 
0.7%
267286.4615 581
 
0.6%
326853.925 575
 
0.6%
Other values (1296) 81543
91.1%
ValueCountFrequency (%)
1861 1
 
< 0.1%
3738 1
 
< 0.1%
4514 1
 
< 0.1%
4804 1
 
< 0.1%
4876 1
 
< 0.1%
6037 1
 
< 0.1%
6284 1
 
< 0.1%
6686 1
 
< 0.1%
8742.333333 7
< 0.1%
9193 1
 
< 0.1%
ValueCountFrequency (%)
918421 1
 
< 0.1%
835745.5 2
 
< 0.1%
726811.25 4
 
< 0.1%
694993 1
 
< 0.1%
668268 3
 
< 0.1%
665476.25 4
 
< 0.1%
634295 1
 
< 0.1%
625671.3333 7
< 0.1%
612032.75 11
< 0.1%
594801 3
 
< 0.1%

NO_FUNCIONARIOS
Real number (ℝ)

Distinct1021
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.93517
Minimum0
Maximum188
Zeros262
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:15.087774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.176471
Q1108.18182
median118.64103
Q3131
95-th percentile146.17391
Maximum188
Range188
Interquartile range (IQR)22.818182

Descriptive statistics

Standard deviation18.06663
Coefficient of variation (CV)0.15190317
Kurtosis4.6349054
Mean118.93517
Median Absolute Deviation (MAD)11.808974
Skewness-0.80313201
Sum10650764
Variance326.40313
MonotonicityNot monotonic
2023-04-25T11:25:16.005107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.8205128 1325
 
1.5%
137.675 1222
 
1.4%
126.1794872 902
 
1.0%
126.4444444 787
 
0.9%
137.95 724
 
0.8%
125.4 660
 
0.7%
98.55 620
 
0.7%
130.45 612
 
0.7%
131 594
 
0.7%
112.15 575
 
0.6%
Other values (1011) 81530
91.0%
ValueCountFrequency (%)
0 262
0.3%
62.33333333 9
 
< 0.1%
68.84210526 237
0.3%
74 4
 
< 0.1%
74.5 2
 
< 0.1%
75.23809524 59
 
0.1%
75.33333333 5
 
< 0.1%
76 1
 
< 0.1%
78 11
 
< 0.1%
79.05263158 95
 
0.1%
ValueCountFrequency (%)
188 3
 
< 0.1%
176 1
 
< 0.1%
175 1
 
< 0.1%
171.2352941 25
 
< 0.1%
169.5641026 141
0.2%
168 5
 
< 0.1%
167 4
 
< 0.1%
166.1818182 19
 
< 0.1%
163.5333333 21
 
< 0.1%
162.3 173
0.2%

PRAZO
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct230
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.200858
Minimum1
Maximum2677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:16.335618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q116
median18
Q324
95-th percentile45
Maximum2677
Range2676
Interquartile range (IQR)8

Descriptive statistics

Standard deviation26.382167
Coefficient of variation (CV)1.1371203
Kurtosis3578.2996
Mean23.200858
Median Absolute Deviation (MAD)2
Skewness48.052044
Sum2077660
Variance696.01872
MonotonicityNot monotonic
2023-04-25T11:25:16.633854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 22389
25.0%
18 11901
13.3%
17 10776
12.0%
22 5644
 
6.3%
19 5329
 
6.0%
20 5251
 
5.9%
25 2913
 
3.3%
21 2772
 
3.1%
30 2149
 
2.4%
36 2043
 
2.3%
Other values (220) 18384
20.5%
ValueCountFrequency (%)
1 5
< 0.1%
2 6
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 4
< 0.1%
12 2
 
< 0.1%
13 4
< 0.1%
ValueCountFrequency (%)
2677 1
< 0.1%
2500 1
< 0.1%
2107 1
< 0.1%
1911 2
< 0.1%
1318 2
< 0.1%
1313 1
< 0.1%
1244 2
< 0.1%
1198 1
< 0.1%
1147 2
< 0.1%
1124 1
< 0.1%

PAGTO_DIFF_VENC
Real number (ℝ)

SKEWED  ZEROS 

Distinct308
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3251555
Minimum-2661
Maximum458
Zeros70609
Zeros (%)78.8%
Negative9139
Negative (%)10.2%
Memory size699.7 KiB
2023-04-25T11:25:16.912246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2661
5-th percentile-3
Q10
median0
Q30
95-th percentile6
Maximum458
Range3119
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.166653
Coefficient of variation (CV)-77.398822
Kurtosis4311.7792
Mean-0.3251555
Median Absolute Deviation (MAD)0
Skewness-52.780067
Sum-29118
Variance633.3604
MonotonicityNot monotonic
2023-04-25T11:25:17.191958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70609
78.8%
-1 3254
 
3.6%
1 2190
 
2.4%
5 1076
 
1.2%
-3 970
 
1.1%
7 790
 
0.9%
6 783
 
0.9%
2 705
 
0.8%
-2 670
 
0.7%
3 620
 
0.7%
Other values (298) 7884
 
8.8%
ValueCountFrequency (%)
-2661 1
< 0.1%
-2483 1
< 0.1%
-2070 1
< 0.1%
-1896 2
< 0.1%
-1303 1
< 0.1%
-1297 1
< 0.1%
-1284 1
< 0.1%
-1229 1
< 0.1%
-1224 1
< 0.1%
-1183 1
< 0.1%
ValueCountFrequency (%)
458 1
< 0.1%
365 1
< 0.1%
331 1
< 0.1%
329 1
< 0.1%
309 1
< 0.1%
306 1
< 0.1%
305 1
< 0.1%
269 2
< 0.1%
267 1
< 0.1%
233 2
< 0.1%

PAGTO_DIFF_EMISSAO
Real number (ℝ)

Distinct271
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.875702
Minimum0
Maximum1074
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:17.486409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q116
median18
Q324
95-th percentile45
Maximum1074
Range1074
Interquartile range (IQR)8

Descriptive statistics

Standard deviation16.363855
Coefficient of variation (CV)0.71533784
Kurtosis372.12087
Mean22.875702
Median Absolute Deviation (MAD)2
Skewness12.882268
Sum2048542
Variance267.77576
MonotonicityNot monotonic
2023-04-25T11:25:17.787065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 21488
24.0%
18 11006
12.3%
17 10508
11.7%
19 5449
 
6.1%
22 5373
 
6.0%
20 4508
 
5.0%
21 3243
 
3.6%
25 2966
 
3.3%
15 2258
 
2.5%
24 1977
 
2.2%
Other values (261) 20775
23.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 5
 
< 0.1%
5 6
< 0.1%
6 4
 
< 0.1%
7 6
< 0.1%
8 14
< 0.1%
9 13
< 0.1%
ValueCountFrequency (%)
1074 1
< 0.1%
624 1
< 0.1%
601 1
< 0.1%
499 1
< 0.1%
495 2
< 0.1%
483 1
< 0.1%
471 1
< 0.1%
457 1
< 0.1%
451 1
< 0.1%
426 1
< 0.1%

INADIMPLENTE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
0
83614 
1
 
5937

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89551
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

Length

2023-04-25T11:25:18.068767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:18.325657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89551
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 89551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83614
93.4%
1 5937
 
6.6%

ZONA_POSTAL
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
PR / SC
14814 
Litoral e interior de SP
14328 
MG
13919 
CE / PI / MA / PA / AP / AM / RR / AC
9766 
DF / GO / RO / TO / MT / MS
9480 
Other values (4)
27244 

Length

Max length37
Median length27
Mean length14.341359
Min length2

Characters and Unicode

Total characters1284283
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCE / PI / MA / PA / AP / AM / RR / AC
2nd rowCE / PI / MA / PA / AP / AM / RR / AC
3rd rowCE / PI / MA / PA / AP / AM / RR / AC
4th rowCE / PI / MA / PA / AP / AM / RR / AC
5th rowCE / PI / MA / PA / AP / AM / RR / AC

Common Values

ValueCountFrequency (%)
PR / SC 14814
16.5%
Litoral e interior de SP 14328
16.0%
MG 13919
15.5%
CE / PI / MA / PA / AP / AM / RR / AC 9766
10.9%
DF / GO / RO / TO / MT / MS 9480
10.6%
RJ / ES 9081
10.1%
RS 7399
8.3%
BH / SE 6978
7.8%
PE / AL / PB / RN 3786
 
4.2%

Length

2023-04-25T11:25:18.545773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:18.875416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
157993
34.1%
pr 14814
 
3.2%
sc 14814
 
3.2%
litoral 14328
 
3.1%
e 14328
 
3.1%
interior 14328
 
3.1%
de 14328
 
3.1%
sp 14328
 
3.1%
mg 13919
 
3.0%
ap 9766
 
2.1%
Other values (22) 179903
38.9%

Most occurring characters

ValueCountFrequency (%)
373298
29.1%
/ 157993
12.3%
P 66012
 
5.1%
R 64092
 
5.0%
S 62080
 
4.8%
A 52616
 
4.1%
M 52411
 
4.1%
e 42984
 
3.3%
i 42984
 
3.3%
r 42984
 
3.3%
Other values (19) 326829
25.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 509416
39.7%
Space Separator 373298
29.1%
Lowercase Letter 243576
19.0%
Other Punctuation 157993
 
12.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 66012
13.0%
R 64092
12.6%
S 62080
12.2%
A 52616
10.3%
M 52411
10.3%
C 34346
6.7%
E 29611
 
5.8%
O 28440
 
5.6%
G 23399
 
4.6%
T 18960
 
3.7%
Other values (8) 77449
15.2%
Lowercase Letter
ValueCountFrequency (%)
e 42984
17.6%
i 42984
17.6%
r 42984
17.6%
o 28656
11.8%
t 28656
11.8%
n 14328
 
5.9%
d 14328
 
5.9%
l 14328
 
5.9%
a 14328
 
5.9%
Space Separator
ValueCountFrequency (%)
373298
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 157993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 752992
58.6%
Common 531291
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 66012
 
8.8%
R 64092
 
8.5%
S 62080
 
8.2%
A 52616
 
7.0%
M 52411
 
7.0%
e 42984
 
5.7%
i 42984
 
5.7%
r 42984
 
5.7%
C 34346
 
4.6%
E 29611
 
3.9%
Other values (17) 262872
34.9%
Common
ValueCountFrequency (%)
373298
70.3%
/ 157993
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1284283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
373298
29.1%
/ 157993
12.3%
P 66012
 
5.1%
R 64092
 
5.0%
S 62080
 
4.8%
A 52616
 
4.1%
M 52411
 
4.1%
e 42984
 
3.3%
i 42984
 
3.3%
r 42984
 
3.3%
Other values (19) 326829
25.4%

LIFETIME_CLIENTE_DIAS
Real number (ℝ)

Distinct7325
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3631.1868
Minimum0
Maximum7773
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:19.218651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile477
Q11865
median3163
Q35453
95-th percentile7514
Maximum7773
Range7773
Interquartile range (IQR)3588

Descriptive statistics

Standard deviation2284.4214
Coefficient of variation (CV)0.62911152
Kurtosis-1.0066461
Mean3631.1868
Median Absolute Deviation (MAD)1508
Skewness0.45131942
Sum3.2517641 × 108
Variance5218581.4
MonotonicityNot monotonic
2023-04-25T11:25:19.529800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3216 59
 
0.1%
7051 57
 
0.1%
2849 55
 
0.1%
7579 51
 
0.1%
6834 50
 
0.1%
2817 50
 
0.1%
3244 49
 
0.1%
3094 48
 
0.1%
6987 47
 
0.1%
3429 45
 
0.1%
Other values (7315) 89040
99.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4
< 0.1%
2 8
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 4
< 0.1%
6 8
< 0.1%
7 3
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
7773 1
 
< 0.1%
7771 2
 
< 0.1%
7770 8
 
< 0.1%
7769 10
< 0.1%
7768 16
< 0.1%
7767 2
 
< 0.1%
7766 9
< 0.1%
7765 21
< 0.1%
7764 21
< 0.1%
7763 16
< 0.1%

MES_EMISSAO_DOCUMENTO
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8395104
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:19.811679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4200993
Coefficient of variation (CV)0.50005031
Kurtosis-1.184017
Mean6.8395104
Median Absolute Deviation (MAD)3
Skewness-0.19811845
Sum612485
Variance11.697079
MonotonicityNot monotonic
2023-04-25T11:25:20.034121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 9620
10.7%
9 9395
10.5%
11 8699
9.7%
8 8176
9.1%
7 7155
8.0%
1 7094
7.9%
12 6833
7.6%
6 6786
7.6%
5 6743
7.5%
3 6541
7.3%
Other values (2) 12509
14.0%
ValueCountFrequency (%)
1 7094
7.9%
2 6316
7.1%
3 6541
7.3%
4 6193
6.9%
5 6743
7.5%
6 6786
7.6%
7 7155
8.0%
8 8176
9.1%
9 9395
10.5%
10 9620
10.7%
ValueCountFrequency (%)
12 6833
7.6%
11 8699
9.7%
10 9620
10.7%
9 9395
10.5%
8 8176
9.1%
7 7155
8.0%
6 6786
7.6%
5 6743
7.5%
4 6193
6.9%
3 6541
7.3%

MES_PAGAMENTO
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9010843
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:20.297715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.476307
Coefficient of variation (CV)0.50373345
Kurtosis-1.2268499
Mean6.9010843
Median Absolute Deviation (MAD)3
Skewness-0.18984154
Sum617999
Variance12.08471
MonotonicityNot monotonic
2023-04-25T11:25:20.529674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 9697
10.8%
11 9330
10.4%
9 8772
9.8%
12 7876
8.8%
7 7324
8.2%
8 7088
7.9%
3 6946
7.8%
1 6804
7.6%
2 6537
7.3%
6 6525
7.3%
Other values (2) 12652
14.1%
ValueCountFrequency (%)
1 6804
7.6%
2 6537
7.3%
3 6946
7.8%
4 6159
6.9%
5 6493
7.3%
6 6525
7.3%
7 7324
8.2%
8 7088
7.9%
9 8772
9.8%
10 9697
10.8%
ValueCountFrequency (%)
12 7876
8.8%
11 9330
10.4%
10 9697
10.8%
9 8772
9.8%
8 7088
7.9%
7 7324
8.2%
6 6525
7.3%
5 6493
7.3%
4 6159
6.9%
3 6946
7.8%

MES_VENCIMENTO
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.892564
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:20.763651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4835713
Coefficient of variation (CV)0.50541008
Kurtosis-1.2300441
Mean6.892564
Median Absolute Deviation (MAD)3
Skewness-0.18949265
Sum617236
Variance12.135269
MonotonicityNot monotonic
2023-04-25T11:25:20.984703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 9699
10.8%
11 9393
10.5%
9 8707
9.7%
12 7840
8.8%
7 7352
8.2%
8 7056
7.9%
1 6980
7.8%
3 6888
7.7%
2 6506
7.3%
6 6468
7.2%
Other values (2) 12662
14.1%
ValueCountFrequency (%)
1 6980
7.8%
2 6506
7.3%
3 6888
7.7%
4 6206
6.9%
5 6456
7.2%
6 6468
7.2%
7 7352
8.2%
8 7056
7.9%
9 8707
9.7%
10 9699
10.8%
ValueCountFrequency (%)
12 7840
8.8%
11 9393
10.5%
10 9699
10.8%
9 8707
9.7%
8 7056
7.9%
7 7352
8.2%
6 6468
7.2%
5 6456
7.2%
4 6206
6.9%
3 6888
7.7%

MES_CADASTRO
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8500966
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:21.226941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median7
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2898736
Coefficient of variation (CV)0.56236228
Kurtosis-1.2942668
Mean5.8500966
Median Absolute Deviation (MAD)3
Skewness0.060644279
Sum523882
Variance10.823269
MonotonicityNot monotonic
2023-04-25T11:25:21.442359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 27264
30.4%
2 23647
26.4%
3 6377
 
7.1%
7 4341
 
4.8%
12 4339
 
4.8%
9 3800
 
4.2%
5 3545
 
4.0%
10 3471
 
3.9%
6 3451
 
3.9%
1 3283
 
3.7%
Other values (2) 6033
 
6.7%
ValueCountFrequency (%)
1 3283
 
3.7%
2 23647
26.4%
3 6377
 
7.1%
4 2871
 
3.2%
5 3545
 
4.0%
6 3451
 
3.9%
7 4341
 
4.8%
8 27264
30.4%
9 3800
 
4.2%
10 3471
 
3.9%
ValueCountFrequency (%)
12 4339
 
4.8%
11 3162
 
3.5%
10 3471
 
3.9%
9 3800
 
4.2%
8 27264
30.4%
7 4341
 
4.8%
6 3451
 
3.9%
5 3545
 
4.0%
4 2871
 
3.2%
3 6377
 
7.1%

MES_SAFRA_REF
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9202242
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:21.678299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4573287
Coefficient of variation (CV)0.49959779
Kurtosis-1.210242
Mean6.9202242
Median Absolute Deviation (MAD)3
Skewness-0.20291361
Sum619713
Variance11.953122
MonotonicityNot monotonic
2023-04-25T11:25:21.897094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 9916
11.1%
9 9132
10.2%
11 9084
10.1%
12 7776
8.7%
7 7243
8.1%
8 7183
8.0%
3 6785
7.6%
1 6664
7.4%
5 6643
7.4%
6 6619
7.4%
Other values (2) 12506
14.0%
ValueCountFrequency (%)
1 6664
7.4%
2 6504
7.3%
3 6785
7.6%
4 6002
6.7%
5 6643
7.4%
6 6619
7.4%
7 7243
8.1%
8 7183
8.0%
9 9132
10.2%
10 9916
11.1%
ValueCountFrequency (%)
12 7776
8.7%
11 9084
10.1%
10 9916
11.1%
9 9132
10.2%
8 7183
8.0%
7 7243
8.1%
6 6619
7.4%
5 6643
7.4%
4 6002
6.7%
3 6785
7.6%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Sunday
16335 
Thursday
16300 
Wednesday
14418 
Monday
14413 
Tuesday
14297 
Other values (2)
13788 

Length

Max length9
Median length8
Mean length7.0524841
Min length6

Characters and Unicode

Total characters631557
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSunday
3rd rowSunday
4th rowThursday
5th rowFriday

Common Values

ValueCountFrequency (%)
Sunday 16335
18.2%
Thursday 16300
18.2%
Wednesday 14418
16.1%
Monday 14413
16.1%
Tuesday 14297
16.0%
Friday 11738
13.1%
Saturday 2050
 
2.3%

Length

2023-04-25T11:25:22.152397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:22.473487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sunday 16335
18.2%
thursday 16300
18.2%
wednesday 14418
16.1%
monday 14413
16.1%
tuesday 14297
16.0%
friday 11738
13.1%
saturday 2050
 
2.3%

Most occurring characters

ValueCountFrequency (%)
d 103969
16.5%
a 91601
14.5%
y 89551
14.2%
u 48982
7.8%
n 45166
7.2%
s 45015
7.1%
e 43133
6.8%
T 30597
 
4.8%
r 30088
 
4.8%
S 18385
 
2.9%
Other values (7) 85070
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 542006
85.8%
Uppercase Letter 89551
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 103969
19.2%
a 91601
16.9%
y 89551
16.5%
u 48982
9.0%
n 45166
8.3%
s 45015
8.3%
e 43133
8.0%
r 30088
 
5.6%
h 16300
 
3.0%
o 14413
 
2.7%
Other values (2) 13788
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
T 30597
34.2%
S 18385
20.5%
W 14418
16.1%
M 14413
16.1%
F 11738
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 631557
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 103969
16.5%
a 91601
14.5%
y 89551
14.2%
u 48982
7.8%
n 45166
7.2%
s 45015
7.1%
e 43133
6.8%
T 30597
 
4.8%
r 30088
 
4.8%
S 18385
 
2.9%
Other values (7) 85070
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 631557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 103969
16.5%
a 91601
14.5%
y 89551
14.2%
u 48982
7.8%
n 45166
7.2%
s 45015
7.1%
e 43133
6.8%
T 30597
 
4.8%
r 30088
 
4.8%
S 18385
 
2.9%
Other values (7) 85070
13.5%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Monday
29887 
Tuesday
16188 
Wednesday
15447 
Thursday
14146 
Friday
13602 
Other values (2)
 
281

Length

Max length9
Median length8
Mean length7.0187826
Min length6

Characters and Unicode

Total characters628539
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowTuesday
3rd rowTuesday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Monday 29887
33.4%
Tuesday 16188
18.1%
Wednesday 15447
17.2%
Thursday 14146
15.8%
Friday 13602
15.2%
Saturday 206
 
0.2%
Sunday 75
 
0.1%

Length

2023-04-25T11:25:22.766098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:23.084508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
monday 29887
33.4%
tuesday 16188
18.1%
wednesday 15447
17.2%
thursday 14146
15.8%
friday 13602
15.2%
saturday 206
 
0.2%
sunday 75
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 104998
16.7%
a 89757
14.3%
y 89551
14.2%
e 47082
7.5%
s 45781
7.3%
n 45409
7.2%
u 30615
 
4.9%
T 30334
 
4.8%
M 29887
 
4.8%
o 29887
 
4.8%
Other values (7) 85238
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 538988
85.8%
Uppercase Letter 89551
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 104998
19.5%
a 89757
16.7%
y 89551
16.6%
e 47082
8.7%
s 45781
8.5%
n 45409
8.4%
u 30615
 
5.7%
o 29887
 
5.5%
r 27954
 
5.2%
h 14146
 
2.6%
Other values (2) 13808
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
T 30334
33.9%
M 29887
33.4%
W 15447
17.2%
F 13602
15.2%
S 281
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 628539
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 104998
16.7%
a 89757
14.3%
y 89551
14.2%
e 47082
7.5%
s 45781
7.3%
n 45409
7.2%
u 30615
 
4.9%
T 30334
 
4.8%
M 29887
 
4.8%
o 29887
 
4.8%
Other values (7) 85238
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 104998
16.7%
a 89757
14.3%
y 89551
14.2%
e 47082
7.5%
s 45781
7.3%
n 45409
7.2%
u 30615
 
4.9%
T 30334
 
4.8%
M 29887
 
4.8%
o 29887
 
4.8%
Other values (7) 85238
13.6%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Monday
30976 
Wednesday
15506 
Tuesday
15438 
Thursday
14347 
Friday
12915 
Other values (2)
 
369

Length

Max length9
Median length8
Mean length7.0190618
Min length6

Characters and Unicode

Total characters628564
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowMonday
3rd rowMonday
4th rowFriday
5th rowThursday

Common Values

ValueCountFrequency (%)
Monday 30976
34.6%
Wednesday 15506
17.3%
Tuesday 15438
17.2%
Thursday 14347
16.0%
Friday 12915
14.4%
Saturday 304
 
0.3%
Sunday 65
 
0.1%

Length

2023-04-25T11:25:23.393599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:23.713467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
monday 30976
34.6%
wednesday 15506
17.3%
tuesday 15438
17.2%
thursday 14347
16.0%
friday 12915
14.4%
saturday 304
 
0.3%
sunday 65
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 105057
16.7%
a 89855
14.3%
y 89551
14.2%
n 46547
7.4%
e 46450
7.4%
s 45291
7.2%
o 30976
 
4.9%
M 30976
 
4.9%
u 30154
 
4.8%
T 29785
 
4.7%
Other values (7) 83922
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 539013
85.8%
Uppercase Letter 89551
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 105057
19.5%
a 89855
16.7%
y 89551
16.6%
n 46547
8.6%
e 46450
8.6%
s 45291
8.4%
o 30976
 
5.7%
u 30154
 
5.6%
r 27566
 
5.1%
h 14347
 
2.7%
Other values (2) 13219
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
M 30976
34.6%
T 29785
33.3%
W 15506
17.3%
F 12915
14.4%
S 369
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 628564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 105057
16.7%
a 89855
14.3%
y 89551
14.2%
n 46547
7.4%
e 46450
7.4%
s 45291
7.2%
o 30976
 
4.9%
M 30976
 
4.9%
u 30154
 
4.8%
T 29785
 
4.7%
Other values (7) 83922
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 105057
16.7%
a 89855
14.3%
y 89551
14.2%
n 46547
7.4%
e 46450
7.4%
s 45291
7.2%
o 30976
 
4.9%
M 30976
 
4.9%
u 30154
 
4.8%
T 29785
 
4.7%
Other values (7) 83922
13.4%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Tuesday
31898 
Monday
27919 
Sunday
12273 
Thursday
9380 
Wednesday
8005 

Length

Max length9
Median length8
Mean length6.83386
Min length6

Characters and Unicode

Total characters611979
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Tuesday 31898
35.6%
Monday 27919
31.2%
Sunday 12273
 
13.7%
Thursday 9380
 
10.5%
Wednesday 8005
 
8.9%
Friday 76
 
0.1%

Length

2023-04-25T11:25:24.013984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:24.336624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 31898
35.6%
monday 27919
31.2%
sunday 12273
 
13.7%
thursday 9380
 
10.5%
wednesday 8005
 
8.9%
friday 76
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 97556
15.9%
a 89551
14.6%
y 89551
14.6%
u 53551
8.8%
s 49283
8.1%
n 48197
7.9%
e 47908
7.8%
T 41278
6.7%
M 27919
 
4.6%
o 27919
 
4.6%
Other values (6) 39266
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 522428
85.4%
Uppercase Letter 89551
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 97556
18.7%
a 89551
17.1%
y 89551
17.1%
u 53551
10.3%
s 49283
9.4%
n 48197
9.2%
e 47908
9.2%
o 27919
 
5.3%
r 9456
 
1.8%
h 9380
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 41278
46.1%
M 27919
31.2%
S 12273
 
13.7%
W 8005
 
8.9%
F 76
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 611979
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 97556
15.9%
a 89551
14.6%
y 89551
14.6%
u 53551
8.8%
s 49283
8.1%
n 48197
7.9%
e 47908
7.8%
T 41278
6.7%
M 27919
 
4.6%
o 27919
 
4.6%
Other values (6) 39266
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 611979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 97556
15.9%
a 89551
14.6%
y 89551
14.6%
u 53551
8.8%
s 49283
8.1%
n 48197
7.9%
e 47908
7.8%
T 41278
6.7%
M 27919
 
4.6%
o 27919
 
4.6%
Other values (6) 39266
6.4%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
Monday
15745 
Friday
13442 
Saturday
13281 
Sunday
12010 
Tuesday
11877 
Other values (2)
23196 

Length

Max length9
Median length8
Mean length7.0761019
Min length6

Characters and Unicode

Total characters633672
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSaturday
3rd rowSaturday
4th rowSaturday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Monday 15745
17.6%
Friday 13442
15.0%
Saturday 13281
14.8%
Sunday 12010
13.4%
Tuesday 11877
13.3%
Thursday 11661
13.0%
Wednesday 11535
12.9%

Length

2023-04-25T11:25:24.722663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:25.255076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
monday 15745
17.6%
friday 13442
15.0%
saturday 13281
14.8%
sunday 12010
13.4%
tuesday 11877
13.3%
thursday 11661
13.0%
wednesday 11535
12.9%

Most occurring characters

ValueCountFrequency (%)
a 102832
16.2%
d 101086
16.0%
y 89551
14.1%
u 48829
7.7%
n 39290
 
6.2%
r 38384
 
6.1%
s 35073
 
5.5%
e 34947
 
5.5%
S 25291
 
4.0%
T 23538
 
3.7%
Other values (7) 94851
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 544121
85.9%
Uppercase Letter 89551
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 102832
18.9%
d 101086
18.6%
y 89551
16.5%
u 48829
9.0%
n 39290
 
7.2%
r 38384
 
7.1%
s 35073
 
6.4%
e 34947
 
6.4%
o 15745
 
2.9%
i 13442
 
2.5%
Other values (2) 24942
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 25291
28.2%
T 23538
26.3%
M 15745
17.6%
F 13442
15.0%
W 11535
12.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 633672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 102832
16.2%
d 101086
16.0%
y 89551
14.1%
u 48829
7.7%
n 39290
 
6.2%
r 38384
 
6.1%
s 35073
 
5.5%
e 34947
 
5.5%
S 25291
 
4.0%
T 23538
 
3.7%
Other values (7) 94851
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 102832
16.2%
d 101086
16.0%
y 89551
14.1%
u 48829
7.7%
n 39290
 
6.2%
r 38384
 
6.1%
s 35073
 
5.5%
e 34947
 
5.5%
S 25291
 
4.0%
T 23538
 
3.7%
Other values (7) 94851
15.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
2019
27393 
2021
26479 
2020
26426 
2018
9253 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters358204
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 27393
30.6%
2021 26479
29.6%
2020 26426
29.5%
2018 9253
 
10.3%

Length

2023-04-25T11:25:25.754026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:26.241202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2019 27393
30.6%
2021 26479
29.6%
2020 26426
29.5%
2018 9253
 
10.3%

Most occurring characters

ValueCountFrequency (%)
2 142456
39.8%
0 115977
32.4%
1 63125
17.6%
9 27393
 
7.6%
8 9253
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358204
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 142456
39.8%
0 115977
32.4%
1 63125
17.6%
9 27393
 
7.6%
8 9253
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 358204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 142456
39.8%
0 115977
32.4%
1 63125
17.6%
9 27393
 
7.6%
8 9253
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 142456
39.8%
0 115977
32.4%
1 63125
17.6%
9 27393
 
7.6%
8 9253
 
2.6%

ANO_PAGAMENTO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
2021
28119 
2019
27034 
2020
26705 
2018
7693 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters358204
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2021 28119
31.4%
2019 27034
30.2%
2020 26705
29.8%
2018 7693
 
8.6%

Length

2023-04-25T11:25:26.716355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:27.130982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2021 28119
31.4%
2019 27034
30.2%
2020 26705
29.8%
2018 7693
 
8.6%

Most occurring characters

ValueCountFrequency (%)
2 144375
40.3%
0 116256
32.5%
1 62846
17.5%
9 27034
 
7.5%
8 7693
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358204
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 144375
40.3%
0 116256
32.5%
1 62846
17.5%
9 27034
 
7.5%
8 7693
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 358204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 144375
40.3%
0 116256
32.5%
1 62846
17.5%
9 27034
 
7.5%
8 7693
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 144375
40.3%
0 116256
32.5%
1 62846
17.5%
9 27034
 
7.5%
8 7693
 
2.1%

ANO_VENCIMENTO
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8428
Minimum2018
Maximum2028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:27.549268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020
Q32021
95-th percentile2021
Maximum2028
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.96874728
Coefficient of variation (CV)0.00047961518
Kurtosis-0.92576954
Mean2019.8428
Median Absolute Deviation (MAD)1
Skewness-0.22432474
Sum1.8087895 × 108
Variance0.93847129
MonotonicityNot monotonic
2023-04-25T11:25:27.972208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2021 28146
31.4%
2019 27062
30.2%
2020 26640
29.7%
2018 7649
 
8.5%
2022 41
 
< 0.1%
2024 8
 
< 0.1%
2023 2
 
< 0.1%
2027 1
 
< 0.1%
2028 1
 
< 0.1%
2025 1
 
< 0.1%
ValueCountFrequency (%)
2018 7649
 
8.5%
2019 27062
30.2%
2020 26640
29.7%
2021 28146
31.4%
2022 41
 
< 0.1%
2023 2
 
< 0.1%
2024 8
 
< 0.1%
2025 1
 
< 0.1%
2027 1
 
< 0.1%
2028 1
 
< 0.1%
ValueCountFrequency (%)
2028 1
 
< 0.1%
2027 1
 
< 0.1%
2025 1
 
< 0.1%
2024 8
 
< 0.1%
2023 2
 
< 0.1%
2022 41
 
< 0.1%
2021 28146
31.4%
2020 26640
29.7%
2019 27062
30.2%
2018 7649
 
8.5%

ANO_CADASTRO
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.925
Minimum2000
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.7 KiB
2023-04-25T11:25:28.405346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12005
median2011
Q32015
95-th percentile2019
Maximum2021
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3211371
Coefficient of variation (CV)0.0031449617
Kurtosis-1.002765
Mean2009.925
Median Absolute Deviation (MAD)4
Skewness-0.46774171
Sum1.7999079 × 108
Variance39.956775
MonotonicityNot monotonic
2023-04-25T11:25:28.865101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2011 21282
23.8%
2000 20620
23.0%
2014 7713
 
8.6%
2015 5435
 
6.1%
2017 5081
 
5.7%
2018 4333
 
4.8%
2012 4083
 
4.6%
2016 3308
 
3.7%
2013 3242
 
3.6%
2009 2557
 
2.9%
Other values (12) 11897
13.3%
ValueCountFrequency (%)
2000 20620
23.0%
2001 167
 
0.2%
2002 157
 
0.2%
2003 208
 
0.2%
2004 754
 
0.8%
2005 743
 
0.8%
2006 1984
 
2.2%
2007 1633
 
1.8%
2008 1415
 
1.6%
2009 2557
 
2.9%
ValueCountFrequency (%)
2021 402
 
0.4%
2020 1629
 
1.8%
2019 2463
 
2.8%
2018 4333
4.8%
2017 5081
5.7%
2016 3308
3.7%
2015 5435
6.1%
2014 7713
8.6%
2013 3242
3.6%
2012 4083
4.6%

ANO_SAFRA_REF
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size699.7 KiB
2021
27505 
2019
27265 
2020
26449 
2018
8332 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters358204
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2021 27505
30.7%
2019 27265
30.4%
2020 26449
29.5%
2018 8332
 
9.3%

Length

2023-04-25T11:25:29.312311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:25:30.393515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2021 27505
30.7%
2019 27265
30.4%
2020 26449
29.5%
2018 8332
 
9.3%

Most occurring characters

ValueCountFrequency (%)
2 143505
40.1%
0 116000
32.4%
1 63102
17.6%
9 27265
 
7.6%
8 8332
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358204
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 143505
40.1%
0 116000
32.4%
1 63102
17.6%
9 27265
 
7.6%
8 8332
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 358204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 143505
40.1%
0 116000
32.4%
1 63102
17.6%
9 27265
 
7.6%
8 8332
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 143505
40.1%
0 116000
32.4%
1 63102
17.6%
9 27265
 
7.6%
8 8332
 
2.3%

Interactions

2023-04-25T11:25:00.354920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:43.847841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:49.267169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:54.299775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:58.434634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:04.832365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:10.350325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:14.767229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:19.252067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:25.320136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:29.673074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:34.160277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:40.874687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:45.223051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:49.319493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:55.129768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:00.618835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:44.105326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:49.674211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:54.541855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:58.686442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:05.223372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:10.608164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:15.025971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:19.631982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:25.572608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:30.289191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:34.418779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:41.300393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:45.462429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:50.041140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:55.521809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:00.863467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:44.354595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:50.067820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:54.795716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:58.955085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:05.615190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:10.853058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:15.275818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:20.022222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:25.843756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:30.539647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:34.671457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:41.626390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:45.712893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:50.282510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:55.906562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:01.103190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:44.614346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:50.465801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:55.036764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:59.207026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:05.982476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:11.100065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:15.530841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:20.431490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:26.107457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:30.794274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:35.001810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:41.882018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:45.968530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:50.526063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:56.295805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:01.334945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:44.871767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:50.894399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:55.287226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:59.461538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:06.401935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:11.338012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:15.805099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:20.846801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:26.369007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:31.039138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:35.389089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:42.135322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:46.224566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:50.763350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:56.681066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:01.602245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:45.133305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:51.288276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:55.552997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:59.742749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:06.842650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:11.605734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:16.067935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:21.279610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:26.657733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:31.323137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:35.756518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:42.399389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:46.488373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:51.033069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:57.137703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:01.860910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:45.403482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:51.662786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:55.833591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:01.300111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:07.251053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:11.866012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:16.315531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:21.684621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:26.922204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:31.576429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:36.142542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:42.653788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:46.736837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:51.286249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:57.577052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:02.117537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:45.754543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:51.982222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:56.114532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:01.558075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:07.720178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:12.126173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:16.583287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:22.033497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:27.197110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:31.846736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:36.506618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:42.926249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:47.008963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:51.570951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:57.966638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:02.360807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:46.128881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:52.232993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:56.365584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:01.816129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:08.068855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:12.372010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:16.835885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:22.414710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:27.456172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:32.097793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:36.910592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:43.173164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:47.254967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:51.934793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:58.216459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:02.643983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:46.564968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:52.500190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:56.650065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:02.159791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:08.494294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:12.663954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:17.128847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:22.874088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:27.739087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:32.377352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:37.338544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:43.447666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:47.529041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:52.354403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:58.496346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:02.908676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:46.937385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:52.761702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:56.919219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:02.511879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:08.759750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:12.929935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:17.390545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:23.299182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:28.044930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:32.629991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:37.751057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:43.706011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:47.778824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:52.757870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:58.753160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:03.173177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:47.305728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:53.015505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:57.169637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:02.926270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:09.031250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:13.177857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:17.666060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:23.732407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:28.320197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:32.891693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:38.162534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:43.965473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:48.044163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:53.161872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:59.047953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:03.425928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:47.717023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:53.280386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:57.415926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:03.288393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:09.308270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:13.423537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:17.928934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:24.123887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:28.580276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:33.153740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:38.517324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:44.216610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:48.289570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:53.575548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:59.301229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:03.709208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:48.090587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:53.536681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:57.672654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:03.700833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:09.567006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:13.697455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:18.191223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:24.496713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:28.859099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:33.402256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:38.940876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:44.461997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:48.550619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:53.989248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:59.563572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:03.951088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:48.493604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:53.804769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:57.943094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:04.075577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:09.829053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:13.956965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:18.463810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:24.794635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:29.137867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:33.658099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:39.471726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:44.712878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:48.802609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:54.336718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:59.827349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:04.369111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:48.914011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:54.057752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:23:58.193718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:04.485838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:10.093181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:14.518878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:18.836783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:25.061549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:29.407917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:33.913996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:40.279378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:44.978032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:49.074583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:24:54.741851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-25T11:25:00.087931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-25T11:25:31.079616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ID_CLIENTEVALOR_A_PAGARCEP_2_DIGRENDA_MES_ANTERIORNO_FUNCIONARIOSPRAZOPAGTO_DIFF_VENCPAGTO_DIFF_EMISSAOLIFETIME_CLIENTE_DIASMES_EMISSAO_DOCUMENTOMES_PAGAMENTOMES_VENCIMENTOMES_CADASTROMES_SAFRA_REFANO_VENCIMENTOANO_CADASTROTAXAFLAG_PFSEGMENTO_INDUSTRIALDOMINIO_EMAILPORTEINADIMPLENTEZONA_POSTALDIASEMANA_EMISSAODIASEMANA_PAGAMENTODIASEMANA_VENCIMENTODIASEMANA_CADASTRODIASEMANA_SAFRA_REFANO_EMISSAO_DOCUMENTOANO_PAGAMENTOANO_SAFRA_REF
ID_CLIENTE1.0000.0090.047-0.0340.327-0.059-0.005-0.059-0.0530.001-0.000-0.005-0.0410.0000.0050.0570.0280.0290.1310.1330.1980.0570.1420.0120.0160.0280.1210.0090.0240.0220.024
VALOR_A_PAGAR0.0091.000-0.0040.0190.001-0.070-0.192-0.1170.1090.0230.0410.0460.0230.0320.118-0.0800.0000.0000.0000.0000.0030.0420.0040.0090.0370.0500.0030.0010.0070.0070.005
CEP_2_DIG0.047-0.0041.000-0.052-0.004-0.0930.020-0.086-0.203-0.012-0.008-0.008-0.098-0.0140.0030.2130.0270.0940.1170.1400.1290.1300.8660.0290.0170.0260.1660.0050.0190.0160.019
RENDA_MES_ANTERIOR-0.0340.019-0.0521.0000.012-0.085-0.116-0.1220.0500.005-0.005-0.003-0.0210.0050.012-0.0450.0170.9160.2190.0550.0490.2000.0730.0090.0200.0120.0990.0080.0200.0210.020
NO_FUNCIONARIOS0.3270.001-0.0040.0121.000-0.0300.025-0.0260.002-0.000-0.000-0.0030.0020.0030.0390.0070.0241.0000.2580.1050.1620.0570.1130.0140.0230.0320.0980.0090.0290.0290.030
PRAZO-0.059-0.070-0.093-0.085-0.0301.0000.0040.9160.052-0.008-0.011-0.0150.082-0.0040.011-0.0590.0110.0000.0180.0000.0050.0400.0060.0040.0210.0830.0000.0040.0090.0100.010
PAGTO_DIFF_VENC-0.005-0.1920.020-0.1160.0250.0041.0000.278-0.070-0.020-0.035-0.039-0.025-0.016-0.0160.0630.0080.0000.0250.0330.0180.1220.0220.0070.0150.0980.0250.0050.0110.0140.011
PAGTO_DIFF_EMISSAO-0.059-0.117-0.086-0.122-0.0260.9160.2781.0000.026-0.016-0.017-0.0190.067-0.0090.001-0.0340.0110.0050.0210.0170.0220.2550.0200.0090.0220.0140.0130.0100.0200.0230.021
LIFETIME_CLIENTE_DIAS-0.0530.109-0.2030.0500.0020.052-0.0700.0261.0000.0110.0050.0080.0940.0150.116-0.9740.0260.0690.0900.1200.1110.0800.1460.0170.0190.0230.3980.0680.2810.2910.287
MES_EMISSAO_DOCUMENTO0.0010.023-0.0120.005-0.000-0.008-0.020-0.0160.0111.0000.6910.681-0.0010.810-0.166-0.0090.0120.0080.0050.0080.0070.0460.0090.0300.0280.0320.0090.2710.2310.1980.208
MES_PAGAMENTO-0.0000.041-0.008-0.005-0.000-0.011-0.035-0.0170.0050.6911.0000.971-0.0040.877-0.1900.0100.0170.0070.0060.0080.0000.0660.0090.0230.0310.0310.0100.3030.2020.2260.210
MES_VENCIMENTO-0.0050.046-0.008-0.003-0.003-0.015-0.039-0.0190.0080.6810.9711.000-0.0040.866-0.1970.0070.0180.0070.0050.0080.0000.0690.0080.0250.0300.0330.0100.3000.2010.2230.209
MES_CADASTRO-0.0410.023-0.098-0.0210.0020.082-0.0250.0670.094-0.001-0.004-0.0041.000-0.0000.009-0.1240.0240.0590.1200.1300.1950.0850.1690.0230.0160.0250.3640.0090.0350.0340.035
MES_SAFRA_REF0.0000.032-0.0140.0050.003-0.004-0.016-0.0090.0150.8100.8770.866-0.0001.000-0.189-0.0060.0210.0060.0060.0080.0050.0580.0100.0300.0280.0310.0100.3880.2160.2220.234
ANO_VENCIMENTO0.0050.1180.0030.0120.0390.011-0.0160.0010.116-0.166-0.190-0.1970.009-0.1891.0000.0840.0140.0060.0270.0170.0170.0430.0150.0130.0140.0850.0200.1390.7670.8110.795
ANO_CADASTRO0.057-0.0800.213-0.0450.007-0.0590.063-0.034-0.974-0.0090.0100.007-0.124-0.0060.0841.0000.0230.0690.1390.1540.1790.1120.1710.0190.0180.0280.4320.0170.1040.1030.104
TAXA0.0280.0000.0270.0170.0240.0110.0080.0110.0260.0120.0170.0180.0240.0210.0140.0231.0000.0080.0130.0210.0210.0160.0240.0000.0060.0070.0160.0220.0210.0190.020
FLAG_PF0.0290.0000.0940.9161.0000.0000.0000.0050.0690.0080.0070.0070.0590.0060.0060.0690.0081.0000.3980.0310.0170.0320.0830.0110.0130.0070.0390.0000.0110.0100.011
SEGMENTO_INDUSTRIAL0.1310.0000.1170.2190.2580.0180.0250.0210.0900.0050.0060.0050.1200.0060.0270.1390.0130.3981.0000.1260.0990.0640.1080.0200.0170.0220.0770.0030.0180.0170.019
DOMINIO_EMAIL0.1330.0000.1400.0550.1050.0000.0330.0170.1200.0080.0080.0080.1300.0080.0170.1540.0210.0310.1261.0000.0950.0590.1360.0160.0150.0170.0980.0080.0280.0260.028
PORTE0.1980.0030.1290.0490.1620.0050.0180.0220.1110.0070.0000.0000.1950.0050.0170.1790.0210.0170.0990.0951.0000.0880.1130.0130.0160.0450.1060.0040.0150.0130.016
INADIMPLENTE0.0570.0420.1300.2000.0570.0400.1220.2550.0800.0460.0660.0690.0850.0580.0430.1120.0160.0320.0640.0590.0881.0000.1110.0300.0490.0660.0960.0570.0380.0470.037
ZONA_POSTAL0.1420.0040.8660.0730.1130.0060.0220.0200.1460.0090.0090.0080.1690.0100.0150.1710.0240.0830.1080.1360.1130.1111.0000.0310.0160.0250.1620.0070.0230.0210.023
DIASEMANA_EMISSAO0.0120.0090.0290.0090.0140.0040.0070.0090.0170.0300.0230.0250.0230.0300.0130.0190.0000.0110.0200.0160.0130.0300.0311.0000.2760.3090.0180.0400.0220.0200.023
DIASEMANA_PAGAMENTO0.0160.0370.0170.0200.0230.0210.0150.0220.0190.0280.0310.0300.0160.0280.0140.0180.0060.0130.0170.0150.0160.0490.0160.2761.0000.6210.0140.0210.0250.0200.026
DIASEMANA_VENCIMENTO0.0280.0500.0260.0120.0320.0830.0980.0140.0230.0320.0310.0330.0250.0310.0850.0280.0070.0070.0220.0170.0450.0660.0250.3090.6211.0000.0220.0250.0280.0220.029
DIASEMANA_CADASTRO0.1210.0030.1660.0990.0980.0000.0250.0130.3980.0090.0100.0100.3640.0100.0200.4320.0160.0390.0770.0980.1060.0960.1620.0180.0140.0221.0000.0060.0290.0250.028
DIASEMANA_SAFRA_REF0.0090.0010.0050.0080.0090.0040.0050.0100.0680.2710.3030.3000.0090.3880.1390.0170.0220.0000.0030.0080.0040.0570.0070.0400.0210.0250.0061.0000.2730.2880.307
ANO_EMISSAO_DOCUMENTO0.0240.0070.0190.0200.0290.0090.0110.0200.2810.2310.2020.2010.0350.2160.7670.1040.0210.0110.0180.0280.0150.0380.0230.0220.0250.0280.0290.2731.0000.9200.952
ANO_PAGAMENTO0.0220.0070.0160.0210.0290.0100.0140.0230.2910.1980.2260.2230.0340.2220.8110.1030.0190.0100.0170.0260.0130.0470.0210.0200.0200.0220.0250.2880.9201.0000.965
ANO_SAFRA_REF0.0240.0050.0190.0200.0300.0100.0110.0210.2870.2080.2100.2090.0350.2340.7950.1040.0200.0110.0190.0280.0160.0370.0230.0230.0260.0290.0280.3070.9520.9651.000

Missing values

2023-04-25T11:25:05.234869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T11:25:06.372293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_CLIENTEVALOR_A_PAGARTAXAFLAG_PFSEGMENTO_INDUSTRIALDOMINIO_EMAILPORTECEP_2_DIGRENDA_MES_ANTERIORNO_FUNCIONARIOSPRAZOPAGTO_DIFF_VENCPAGTO_DIFF_EMISSAOINADIMPLENTEZONA_POSTALLIFETIME_CLIENTE_DIASMES_EMISSAO_DOCUMENTOMES_PAGAMENTOMES_VENCIMENTOMES_CADASTROMES_SAFRA_REFDIASEMANA_EMISSAODIASEMANA_PAGAMENTODIASEMANA_VENCIMENTODIASEMANA_CADASTRODIASEMANA_SAFRA_REFANO_EMISSAO_DOCUMENTOANO_PAGAMENTOANO_VENCIMENTOANO_CADASTROANO_SAFRA_REF
0166124039590323067635516.416.99PJServiçosYAHOOPEQUENO65290074.13888999.361111200200CE / PI / MA / PA / AP / AM / RR / AC182189989FridayThursdayThursdayThursdaySaturday20182018201820132018
1166124039590323067617758.216.99PJServiçosYAHOOPEQUENO65290074.13888999.361111221230CE / PI / MA / PA / AP / AM / RR / AC182389989SundayTuesdayMondayThursdaySaturday20182018201820132018
2166124039590323067617431.966.99PJServiçosYAHOOPEQUENO65290074.13888999.361111221230CE / PI / MA / PA / AP / AM / RR / AC183089989SundayTuesdayMondayThursdaySaturday20182018201820132018
316612403959032306761341.006.99PJServiçosYAHOOPEQUENO65290074.13888999.361111366421CE / PI / MA / PA / AP / AM / RR / AC18348101089ThursdayThursdayFridayThursdaySaturday20182018201820132018
4166124039590323067621309.856.99PJServiçosYAHOOPEQUENO65290074.13888999.361111200200CE / PI / MA / PA / AP / AM / RR / AC183589989FridayThursdayThursdayThursdaySaturday20182018201820132018
5166124039590323067622427.255.99PJServiçosYAHOOPEQUENO65290074.13888999.361111221230CE / PI / MA / PA / AP / AM / RR / AC185191010810SundayTuesdayMondayThursdayMonday20182018201820132018
6166124039590323067635608.115.99PJServiçosYAHOOPEQUENO65290074.13888999.361111220220CE / PI / MA / PA / AP / AM / RR / AC185891010810SundayMondayMondayThursdayMonday20182018201820132018
7166124039590323067617988.495.99PJServiçosYAHOOPEQUENO65290074.13888999.361111211220CE / PI / MA / PA / AP / AM / RR / AC1873101010810MondayTuesdayMondayThursdayMonday20182018201820132018
8166124039590323067641998.206.99PJServiçosYAHOOPEQUENO65290074.13888999.361111201210CE / PI / MA / PA / AP / AM / RR / AC1882101111811WednesdayWednesdayTuesdayThursdayThursday20182018201820132018
9166124039590323067635514.416.99PJServiçosYAHOOPEQUENO65290074.13888999.361111220220CE / PI / MA / PA / AP / AM / RR / AC1886101111811SundayMondayMondayThursdayThursday20182018201820132018
ID_CLIENTEVALOR_A_PAGARTAXAFLAG_PFSEGMENTO_INDUSTRIALDOMINIO_EMAILPORTECEP_2_DIGRENDA_MES_ANTERIORNO_FUNCIONARIOSPRAZOPAGTO_DIFF_VENCPAGTO_DIFF_EMISSAOINADIMPLENTEZONA_POSTALLIFETIME_CLIENTE_DIASMES_EMISSAO_DOCUMENTOMES_PAGAMENTOMES_VENCIMENTOMES_CADASTROMES_SAFRA_REFDIASEMANA_EMISSAODIASEMANA_PAGAMENTODIASEMANA_VENCIMENTODIASEMANA_CADASTRODIASEMANA_SAFRA_REFANO_EMISSAO_DOCUMENTOANO_PAGAMENTOANO_VENCIMENTOANO_CADASTROANO_SAFRA_REF
895416915663759082844184336000.005.99PJComércioYAHOOGRANDE28207522.0136.046-6400RJ / ES217101212311SundayFridayThursdaySundayMonday20212021202120212021
89542802703527262270101365004.006.99PFSEM_VALORAOLMEDIO7812540.00.036-1350DF / GO / RO / TO / MT / MS978111212211MondayMondayTuesdayWednesdayMonday20212021202120192021
89543738741940781735682554157.586.99PJComércioGMAILPEQUENO91322336.0139.0200200RS7749111111811TuesdayMondayMondayTuesdayMonday20212021202120002021
89544508245570081863834248600.094.99PJComércioBOLMEDIO14374662.0110.0300300Litoral e interior de SP995111212211MondayWednesdayWednesdaySundayMonday20212021202120192021
89545343142688992462482123950.106.99PJServiçosHOTMAILMEDIO69227342.0125.0180180CE / PI / MA / PA / AP / AM / RR / AC455111111811ThursdayMondayMondayThursdayMonday20212021202120202021
89546343142688992462482156879.106.99PJServiçosHOTMAILMEDIO69227342.0125.0170170CE / PI / MA / PA / AP / AM / RR / AC456111111811FridayMondayMondayThursdayMonday20212021202120202021
895475288503299611498087156725.155.99PJComércioYAHOOPEQUENO13352642.0137.017-1160Litoral e interior de SP3761111121111SundayTuesdayWednesdayTuesdayMonday20212021202120202021
89548957773253650890560266.085.99PJComércioGMAILMEDIO20433808.0121.0220220RJ / ES132111212711SundayMondayMondayMondayMonday20212021202120212021
895496094038865287329652301.498.99PJServiçosGMAILGRANDE48532236.0136.0220220BH / SE132111212711SundayMondayMondayMondayMonday20212021202120212021
89550159258178406115760923214.506.99PJComércioSEM_VALORMEDIO90268024.0125.0200200RS7126111212512TuesdayMondayMondayTuesdayWednesday20212021202120022021